Spatially variant immune infiltration scoring in human cancer tissues

The Immunoscore is a method to quantify the immune cell infiltration within cancers to predict the disease prognosis. Previous immune profiling approaches relied on limited immune markers to establish patients’ tumor immunity. However, immune cells exhibit a higher-level complexity that is typically not obtained by the conventional immunohistochemistry methods. Herein, we present a spatially variant immune infiltration score, termed as SpatialVizScore, to quantify immune cells infiltration within lung tumor samples using multiplex protein imaging data. Imaging mass cytometry (IMC) was used to target 26 markers in tumors to identify stromal, immune, and cancer cell states within 26 human tissues from lung cancer patients. Unsupervised clustering methods dissected the spatial infiltration of cells in tissue using the high-dimensional analysis of 16 immune markers and other cancer and stroma enriched labels to profile alterations in the tumors’ immune infiltration patterns. Spatially resolved maps of distinct tumors determined the spatial proximity and neighborhoods of immune-cancer cell pairs. These SpatialVizScore maps provided a ranking of patients’ tumors consisting of immune inflamed, immune suppressed, and immune cold states, demonstrating the tumor’s immune continuum assigned to three distinct infiltration score ranges. Several inflammatory and suppressive immune markers were used to establish the cell-based scoring schemes at the single-cell and pixel-level, depicting the cellular spectra in diverse lung tissues. Thus, SpatialVizScore is an emerging quantitative method to deeply study tumor immunology in cancer tissues.

e Marker abundance heatmap presented the cluster distributions for all patients' tissues. Figure 28. Tissue neighborhood analysis was demonstrated using single-cell data to identify the tumor/epithelial, stromal, and immune composition of patients' tissues.

Supplementary
a Representative schematic provided the process of neighborhood analysis. Each cell is assigned to a distinct type (tumor/epithelial, stroma, CD8+, or CD68+) based on the highest intensity values of markers. Tumor cells (T) were marked by the expression of pankeratin and e-cadherin, and stromal cells (S) were marked by the expression of collagen Type 1 and SMA. Additional cells were marked by the expression of CD8ɑ and CD68. T-marked cells may also be major epithelial cells in paracancerous/adjacent tissues. A Cell network was generated by connecting every cell centroid to its neighboring centroids within a 30-µm distance. Created with BioRender.com a Pre-identified marker images were used to mark distinct tissue regions (Tumor/epithelial: pankeratin and ecadherin, Stroma: SMA and collagen Type 1, CD8ɑ, and CD68) resulting in 4 final images. The maximum projection image is generated by dividing each ROI image into patches with pixel size = 5-µm where each patch is assigned a tissue region based on the maximum intensity value of the pre-identified markers list. Pixel neighborhood analysis was then performed within the unique tissue regions.
b Maximum projection images of ROIs were shown using magenta for tumor/epithelial regions, yellow for stromal regions, green for CD68+ regions, and blue for CD8+ regions. Scale bars represent 200-µm. c Representative schematic provided the difference between cell-based and pixel-based immunoscoring. The cellbased immunoscore relies on cell segmentation masks to identify individual cells. The cell network is generated by identifying the distance between cell centroids within a diameter of 30 µm distance. The pixel-level immunoscore relies on the pixel-based classification where the 800 µm x 800 µm patients' tissues are divided into 160x160 square patches with patch size = 5 µm. Each patch is assigned a phenotype based on the highest intensity of marker expression. Similar to the cell network, the pixel network is generated to assess the neighborhood of the anatomical tissue region.

d-f
Correlation heatmaps between all scores for all ROIs were generated by d cell-level segmentation and pixellevel classification, e cell-level segmentation only, and f pixel-level classification only. Macrophages Macrophages can exist in several activation states including M1 and M2. M1 macrophages were associated with proinflammatory and antitumor effects whereas M2 macrophages were associated with anti-inflammatory and tolerogenic properties. CD68 alone can't differentiate between the two phenotypes, so additional antibodies could be added to the antibody panel to further study their prognostic value and reflect the findings on the immunoscore.

Supplementary Tables
PMID: 29065107 Myeloidderived suppressor cells They were shown to have immunosuppressive capabilities and were shown to support tumor invasion and metastasis. They can be added to the immunoscore to reflect their negative immune modulation nature.
PMID: 29348500 Dendritic cells Given the different phenotypes that make up the dendritic cell population, they are associated with differential prognostic outcomes. CD103+ DCs and CD208+ DCs were associated with positive prognostic values. Thereby, it could be interesting to leverage multiplex panels of antibodies to decipher the differential prognostic values of dendritic cells and reflect that on the immunoscores. It is associated with a positive prognosis for several solid tumors but was associated with reduced survival for NSCLC.